Decisions Over Decimals

Key Concept: We are naturally wired to fear change, so we hide behind requests for more data to delay committing. The “perfect decision” based on perfect data is a mirage.

Analogy/Example: The “Robert Seemore” persona—the colleague who always asks for one more spreadsheet, replacing impactful action with busywork activity.

Key Concept: Data cannot provide both the questions and the answers. You must clarify exactly what you are deciding before pulling data. Explore the unknown rather than just optimizing what is known.

Analogy/Example: Jumping straight into “solutioning” is like digging a hole incredibly fast without checking if you are digging in the right location.

Key Concept: Introduces the IWIK (I Wish I Knew) framework. A 4-step process (Ask, Brainstorm, Capture, Deliberate) to pinpoint the essential minimum viable decision rather than the unachievable perfect one.

Analogy/Example: Using the word “wish” grants stakeholders psychological permission to explore openly without worrying about current budgets, data constraints, or metrics.

Key Concept: Begin with the desired decision or end-state and reverse-engineer the data map required to get there. Build a decision tree first.

Analogy/Example: Writing the blueprint of your final presentation slide (using IWIKs) before doing any mathematical analysis.

Key Concept: Do not accept data at face value. Assess reliability, put it into context, and pressure-test the analysis. Guard against anchoring and confirmation biases.

Analogy/Example: “Torturing the data until it confesses.” Always ask basic context questions: “How was this measured?” “Are averages hiding massive outliers?”

Key Concept: The power of approximation (“Guesstimation”). Don't lose sight of the big picture by arguing over decimals. Statistical significance does not always equal managerial relevance.

Analogy/Example: Consultant case interview math. If you need to know market size, a rapid calculation of magnitude (is it $1M or $100M?) is more useful than a delayed answer of $1,245,678.

Key Concept: Most organizations are terrible at synthesis. Summarizing is just repeating facts. Synthesizing requires connecting the dots, navigating ambiguity, and taking a stance.

Analogy/Example: Applying the “Pyramid Principle.” Communicate the bottom line first. Shift the conversation from “What does the data say?” to “What are we going to do about it?”

Key Concept: Decisions are governed by three dimensions: Time, Risk, and Trust. You must adapt your approach based on these. Understand that most business decisions are reversible.

Analogy/Example: The “Two-Way Door.” If a decision is a two-way door (low risk, high reversibility), make it quickly with less data. Save heavy analysis for one-way doors.

Key Concept: Getting to the answer is only half the battle; the rest is communication. Use a story arc to align your inner voice with external reality. The goal is to compel action, not just inform.

Analogy/Example: Presenting a decision should feel less like reading a phone book of statistics and more like narrating a compelling, hypothesis-driven business case.

Key Concept: How to overcome organizational resistance and ensure decisions actually stick. Use strategies like framing the outcome, reducing scope, and right-sizing the decision-makers.

Analogy/Example: “Seek Consent, Not Consensus.” If you wait for a 100% unanimous vote, you will never move. Seek a level of consent where people disagree but commit to the direction.

Key Concept: Building teams that inherently blend data intelligence with human judgment. Focus on the skill set required to foster a Quantitative Intuition ecosystem.

Analogy/Example: When hiring, do not just look for the smartest data scientist (pure math). Look for the candidate who demonstrates natural curiosity and can translate math into business value.